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Author: search.filters.author.Gaceb, DjamelSubject: search.filters.subject.Deep transfer learning

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    Progressive Deep Transfer Learning for Accurate Glaucoma Detection in Medical Imaging
    (Institute of Electrical and Electronics Engineers, 2024) Yakoub, Assia; Gaceb, Djamel; Touazi, Fayçal; Bourahla, Nourelhouda
    Glaucoma leads to permanent vision disability by damaging the optic nerve, which transmits visual images to the brain. The fact that glaucoma doesn't exhibit any symptoms as it progresses and can't be halted in later stages makes early diagnosis critical. Although various deep learning models have been applied to detect glaucoma from digital fundus images, the scarcity of labeled data has limited their generalization performance, along with their high computational complexity and specialized hardware requirements. In this study, a progressive transfer learning with preprocessing techniques is proposed for the early detection of glaucoma in fundus images. The performance of this approach is compared against transfer learning and convolutional neural networks using three benchmark datasets: Cataract, Glaucoma and Origa. The experimental results demonstrate that reusing pre-trained models from ImageNet and applying them to a database containing the same disease leads to improved performance, compared to using databases with different diseases in progressive transfer learning. Additionally, applying preprocessing techniques to the databases further enhances the results.
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    New CNN stacking model for classification of medical imaging modalities and anatomical organs on medical images
    (CEUR Workshop Proceedings, 2023) Khaled, Mamar; Gaceb, Djamel; Touazi, Fayçal; Aouchiche, Chakib Ammar; Bellouche, Youcef; Titoun, Ayoub
    Decision making in medical diagnosis is tedious and very rigorous task, hence the requirement to use more advanced and intelligent medical imaging diagnostic support systems. The automation of the recognition of medical imaging modalities and human anatomical organs gives these systems the possibility of processing, in an automatic and adapted manner, different types of images in consideration of different medical imaging modalities. It also offers better support to clinicians and patients allowing them to access to more effective image analysis and diagnostic tools. In this context, three deep learning approaches were developed and tested on six different CNN models (VGG16, VGG19, ResNet-50, Xcpetion, Inception and NASNet). Two deep transfer learning modes and an ensemble deep learning algorithm based on stacking were used. The experiments carried out on two datasets of medium and high challenges show very interesting results with F-score reaching 99% for the classification of image modalities and 98% for the classification of anatomical organs.